Recepci´ on y almacenamiento de datos de los dispositivos m´ edicos

1.6. Thesis Outline

Chapter 1 provides an introduction to this dissertation. First, we motivate the problems addressed in this dissertation by reviewing some of the existing challenges in electricity markets. Next, we outline the problems addressed in this thesis. Then, we provide a general overview of the literature relevant to the problems considered in this dissertation. Finally, the approaches and objectives of this thesis are outlined.

Chapter 2 proposes a scheduling model for a system with uncertain power production. If two major facets of a clearing model are considered to be scheduling (technical aspect) and pricing (economic aspect), this chapter focuses on the scheduling problem. The model is formulated as a three-stage stochastic programming problem, where the first stage represents the day-ahead market, the second stage the intra-day market, and the third-stage the real- time operation. We showcase the performance of this model by applying it to an illustrative example and larger case studies, and benchmark it against the market outcomes obtained from a two-stage stochastic market-clearing model.

Chapter 3 proposes pricing methodologies which guarantee cost recovery for units in the presence of non-convexities and uncertainty. Considering two major facets of a clearing model, i.e., scheduling and pricing, this chapter focuses on pricing. The proposed models minimize the duality gap of a stochastic non-convex clearing primal model and the dual problem of a relaxed version of the original primal model subject to primal constraints, dual constraints, cost-recovery constraints, and integrity constraints. The cost-recovery constraints make the proposed problem nonlinear with bi-linear terms. For computational tractability, this problem is linearized and recast as a MILP problem. The proposed models are applied to an illustrative example and larger case studies, and benchmarked against a standard marginal pricing model.

Chapter 4 incorporates flexible demands to a two-stage stochastic market-clearing model, where demand consumption level can be scheduled to some extent by the system operator. The uncertainty related to wind power production is represented via scenarios. We analyze the economic impacts of flexible demands in a system consisting of a generation-mix of comparatively expensive fast-ramping units and comparatively cheap renewable units, which resembles the case of Texas and Spain. The economic assessment of flexible demands include their impacts on operation costs, day-ahead prices, and consequently, consumer payments and producer profits. This assessment is done using a small example and larger case studies. Chapter 5 develops a two-stage MILP model for the Swiss reserve market. The first stage represents the weekly reserve market, and the second stage the daily reserve market. The first-stage variables denote decisions for the acceptance or rejection of indivisible offers in the weekly reserve market, while the second-stage variables are the quantity of reserves to

be procured in the daily market. The decision-making problem is to minimize the expected procurement cost of reserves considering the known offers in the weekly market and the unknown offers in the daily market. We characterize uncertain offers and represent them via scenarios. The results obtained from the implementation of the developed model in the Swiss reserve market are provided. A risk-averse version of the developed model is also formulated and tested.

Chapter 6 provides a summary of the work developed in this thesis accompanied with main conclusions and contributions. Also, some suggestions for future research are listed in this chapter.

Appendix A provides some notions related to a multi-stage stochastic programming.

Appendix B provides the technical characteristics of the IEEE 24-node system used in the case studies of Chapters 2, 3, and 4.

Appendix C provides the mathematical description of minimum up- and down-time con- straints pertaining to the operation conditions of generating units.

2

Multi-Stage Stochastic Market-

Clearing Model

2.1 Introduction

Since the start of the liberalization in the electricity industry in the 90s, the electricity trade has been subject to short-term transactions in the form of pools dealing with daily operations, and future markets pertaining to mid-term and long-term transactions.

The scope of this chapter is short-term pool-based markets, where electricity is traded in a day-ahead market, in a number of intra-day markets (also known as adjustment markets), as well as in real-time operation.

Traditionally, a pool includes a day-ahead market and a real-time one. In the day-ahead market the on-off status of units and their scheduled production levels are determined considering day-ahead forecasts. In real-time operation, there is a need to compensate mismatches between consumption and production in order to preserve the power balance in the system. For this purpose, reserves are scheduled in the day-ahead market to be eventually deployed in real-time operation. In a system with conventional units, the amount of reserves can be easily determined by considering the factors influencing supply-demand mismatch, such as deviation between the day-ahead load forecast and the actual load, the probability of failure of generating units, etc. From an energy trade perspective, conventional units can be scheduled one day in advance without the need for adjusting their scheduled production levels, and hence, there is usually no need for an extra trading floor between the day-ahead market and the real-time power delivery. Therefore, pools consisting of a day-ahead market and a real-time one fit well a system with conventional units. However, the boom in renewable production challenges this traditional setting.

In the day-ahead market, where scheduling is done, the production ability of renewable units is still uncertain, as renewable power production depends on weather, whose day-ahead forecast

still deviates from its actual value. Moving toward real-time operation, a better forecast of weather conditions becomes available that results in a more precise forecast of the renewable power production. Therefore, electricity trades in a horizon between the day-ahead market and real-time operation facilitate the integration of renewable production.

In practice, electricity markets have been evolving to include intra-day trades, where sched- uled production levels in the day-ahead market can be adjusted using the best available information related to weather conditions, and thus, renewable production. The intra-day markets facilitate the integration of renewable production as they give renewable units the opportunity of offering closer to power delivery, and thus, with reduced uncertainty [43] and [32].

The structures of intra-day markets differ on both sides of the Atlantic. While European intra- day markets rely on continuous trade principles (i.e., first come, first serve [21]), a centralized market-clearing mechanism is in favor in the US [26]. The gate closure of intra-day markets may vary from several hours to an hour ahead of power delivery. Also, their clearing horizon may include the whole 24 hours or only some hours [45].

While intra-day markets become the norm, actual clearing processes still rely on deterministic models, which are not suitable for systems with a large amount of renewable production. In such systems, a deterministic model generally results in either under-commitment, which is risky, or over-commitment, which is expensive.

As a solution to deal with uncertain renewable production, mainstream research proposes to apply two-stage stochastic programming framework to the traditional market structure, consisting of the day-ahead market and real-time operation. However, consideration of intra- day markets and their mathematical descriptions in clearing models are missing.

Therefore, the evolving market conditions, involving an increasing number of intra-day mar- kets and large amounts of uncertain renewable production, call for a revision in clearing models.

Since our aim is to take informed day-ahead decisions in a trading environment with an increasing number of intra-day markets and a large amount of renewable production, we believe that the transient from a deterministic clearing model to a stochastic one should be a multi-stage clearing model, and not a two-stage one. The corresponding decision-making processes are described in the following.